Revisiting data-driven dynamic security assessment with a tabular foundation model
O. Arowolo, M. Yang, J.L. Cremer (2026). "Revisiting data-driven dynamic security assessment with a tabular foundation model." SSRN.
CV
Education
- Ph.D. in Electrical Engineering, Delft University of Technology, 2023–present (final year)
- Intelligent Electrical Power Grids group, Delft AI Energy Lab
- Topic: Generalization of Machine Learning Models for Power Systems Applications
- Supervisor: Dr. Jochen Cremer
- M.S. in Electrical and Computer Engineering, Carnegie Mellon University, 2022
- B.Sc. in Electrical and Electronic Engineering, University of Ibadan, 2020
Experience
- 2023–present: PhD researcher, Delft AI Energy Lab, TU Delft, the Netherlands
- 2024–2026: Teaching Assistant, Machine Learning Workflows for Digital Energy Systems, TU Delft, the Netherlands
- Oct–Dec 2022: Teaching Assistant, Carnegie Mellon University Africa, Kigali, Rwanda
- Jul–Sep 2022: Machine Learning Intern (CaCTüS programme), RoLi Lab, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- May–Jul 2022: Data Science Intern, Plentify, Cape Town, South Africa
- May–Jul 2021: Engineering Graduate Trainee, Elektron Energy, Lagos, Nigeria
- 2020–2021: Data Science Instructor, Robotics and Artificial Intelligence Nigeria, Ibadan, Nigeria
Skills
- Machine learning: PyTorch, PyTorch Geometric, Graph neural networks, physics-informed learning, operator learning
- Software engineering: Python, Julia, MATLAB, AWS, Certified Scrum Master, Git
- Data Analytics: Tableau, Power BI, SQL
- Power Systems: Power flow and optimal power flow, time-domain simulation, dynamic security assessment
Publications
Towards generalization of graph neural networks for AC optimal power flow
O. Arowolo, J.L. Cremer (2026). "Towards generalization of graph neural networks for AC optimal power flow." Energy and AI.
Spatio-Temporal Graph Neural Networks for Multi-Period Optimal Power Flow
A. Rajaei, O. Arowolo, J.L. Cremer (2025). "Spatio-Temporal Graph Neural Networks for Multi-Period Optimal Power Flow." 2025 IEEE PES ISGT Europe.
Exploring extrapolation of machine learning models for power system time domain simulation
O. Arowolo, J. Stiasny, J.L. Cremer (2025). "Exploring extrapolation of machine learning models for power system time domain simulation." Sustainable Energy, Grids and Networks.
Learning-Accelerated ADMM for Stochastic Power System Scheduling With Numerous Scenarios
A. Rajaei, O. Arowolo, J.L. Cremer (2025). "Learning-Accelerated ADMM for Stochastic Power System Scheduling With Numerous Scenarios." IEEE Transactions on Sustainable Energy.
Image Augmentation for Satellite Images
O. Adedeji, P. Owoade, O. Ajayi, and O. Arowolo (2022). "Image Augmentation for Satellite Images." arXiv:2207.14580.
Development of A Human Posture Recognition System for Surveillance Application
O. F. Arowolo, E. O. Arogunjo, D. G. Owolabi, and E. D. Markus (2021). "Development of A Human Posture Recognition System for Surveillance Application." International Journal of Computing and Digital Systems. 10(1), 1191–1197.
Talks
Exploring Extrapolation of Machine Learning Models for Power System Time Domain Simulation
Conference talk at 12th Bulk Power System Dynamics and Control Symposium (IREP 2025), Sorrento, Italy
Predicting the Future Trajectory of Zebrafish with Probabilistic Machine Learning
Symposium talk at 1st Computation and Cognition Summer Internship Symposium, Tübingen, Germany